UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGNSUMMER RESEARCH · 2027

Project S1 · Thrust 01

Finite-key QKD under realistic link and sampling constraints

Model how loss, detector noise, and limited data change the amount of secret key a quantum link can generate.

Python / NumPyFinite statisticsProtocol simulation

The question

Which physical, statistical, and post-processing parameters determine the transition to a positive secret-key length?

Why it matters

Quantum key distribution (QKD) establishes secret material between distant parties. Real experiments collect finite samples and operate with imperfect links, so idealized security calculations are only a starting point.

What you could investigate

  1. Implement a prepare-and-measure and/or entanglement-based QKD model with basis choice, loss, detector noise, and finite samples.
  2. Reproduce a known comparison between asymptotic and finite-key predictions, including parameter estimation, reconciliation leakage, and privacy amplification.
  3. Vary the data allocated to estimation and explore when decoy states change the feasibility of key generation.

What you could create

  • A documented QKD simulator
  • Sensitivity plots and a validated baseline
  • An analysis separating physical, statistical, and post-processing limits

Keep exploring

More in secure communications.

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